.. _datasets: ========================= Dataset loading utilities ========================= .. currentmodule:: sklearn.datasets The ``sklearn.datasets`` package embeds some small toy datasets as introduced in the :ref:`Getting Started ` section. To evaluate the impact of the scale of the dataset (``n_samples`` and ``n_features``) while controlling the statistical properties of the data (typically the correlation and informativeness of the features), it is also possible to generate synthetic data. This package also features helpers to fetch larger datasets commonly used by the machine learning community to benchmark algorithm on data that comes from the 'real world'. General dataset API =================== There are three distinct kinds of dataset interfaces for different types of datasets. The simplest one is the interface for sample images, which is described below in the :ref:`sample_images` section. The dataset generation functions and the svmlight loader share a simplistic interface, returning a tuple ``(X, y)`` consisting of a n_samples x n_features numpy array X and an array of length n_samples containing the targets y. The toy datasets as well as the 'real world' datasets and the datasets fetched from mldata.org have more sophisticated structure. These functions return a ``bunch`` (which is a dictionary that is accessible with the 'dict.key' syntax). All datasets have at least two keys, ``data``, containg an array of shape ``n_samples x n_features`` (except for 20newsgroups) and ``target``, a numpy array of length ``n_features``, containing the targets. The datasets also contain a description in ``DESCR`` and some contain ``feature_names`` and ``target_names``. See the dataset descriptions below for details. Toy datasets ============ scikit-learn comes with a few small standard datasets that do not require to download any file from some external website. .. autosummary:: :toctree: ../modules/generated/ :template: function.rst load_boston load_iris load_diabetes load_digits load_linnerud These datasets are useful to quickly illustrate the behavior of the various algorithms implemented in the scikit. They are however often too small to be representative of real world machine learning tasks. .. _sample_images: Sample images ============= The scikit also embed a couple of sample JPEG images published under Creative Commons license by their authors. Those image can be useful to test algorithms and pipeline on 2D data. .. autosummary:: :toctree: ../modules/generated/ :template: function.rst load_sample_images load_sample_image .. image:: ../auto_examples/cluster/images/plot_color_quantization_1.png :target: ../auto_examples/cluster/plot_color_quantization.html :scale: 30 :align: right .. warning:: The default coding of images is based on the ``uint8`` dtype to spare memory. Often machine learning algorithms work best if the input is converted to a floating point representation first. Also, if you plan to use ``pylab.imshow`` don't forget to scale to the range 0 - 1 as done in the following example. .. topic:: Examples: * :ref:`example_cluster_plot_color_quantization.py` .. _sample_generators: Sample generators ================= In addition, scikit-learn includes various random sample generators that can be used to build artifical datasets of controled size and complexity. .. image:: ../auto_examples/images/plot_random_dataset_1.png :target: ../auto_examples/plot_random_dataset.html :scale: 50 :align: center .. autosummary:: :toctree: ../modules/generated/ :template: function.rst make_classification make_multilabel_classification make_regression make_blobs make_friedman1 make_friedman2 make_friedman3 make_low_rank_matrix make_sparse_coded_signal make_sparse_uncorrelated make_spd_matrix make_swiss_roll make_s_curve make_sparse_spd_matrix .. _libsvm_loader: Datasets in svmlight / libsvm format ==================================== scikit-learn includes utility functions for loading datasets in the svmlight / libsvm format. In this format, each line takes the form ``